{
  "id": "2010.05270",
  "version": "v1",
  "published": "2020-10-11T15:21:21.000Z",
  "updated": "2020-10-11T15:21:21.000Z",
  "title": "A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression",
  "authors": [
    "Vivek Mahato",
    "Pádraig Cunningham"
  ],
  "comment": "3nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (2018)",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "It is well understood that Dynamic Time Warping (DTW) is effective in revealing similarities between time series that do not align perfectly. In this paper, we illustrate this on spectroscopy time-series data. We show that DTW is effective in improving accuracy on a regression task when only a single wavelength is considered. When combined with k-Nearest Neighbour, DTW has the added advantage that it can reveal similarities and differences between samples at the level of the time-series. However, in the problem, we consider here data is available across a spectrum of wavelengths. If aggregate statistics (means, variances) are used across many wavelengths the benefits of DTW are no longer apparent. We present this as another example of a situation where big data trumps sophisticated models in Machine Learning.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-10-11T15:21:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dynamic time warping",
      "time series regression",
      "case-study",
      "big data trumps sophisticated models",
      "wavelength"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}